1. Field of the Invention
The present invention relates to cardiac modeling, and more particularly to a method for creating a 3-D statistical shape model of the left ventricle from sets of sparse 2-D contour inputs.
2. Discussion of Prior Art
Statistical Shape models are powerful tools for morphological analysis and object recovery. They may be employed in classification by standing as a point of comparison, which embodies the common characteristics of a group, DeQuardo J R. et. al., relationships of neuroanatomic landmarks in schizophrenia. Psychiatry Research. 67(1):81-95, 1996 May 31. Statistical Shape models may also be used as the basis for prediction. Given a set of views of an object, Statistical Shape models allow the morphology of unobserved regions to be inferred, Fleute M, Lavallee S. Nonrigid 3-D/2-D registration of images using statistical models. MICCAI'99 Springer-Verlag. 1999, pp.138-47. Berlin, Germany. Indeed, statistical shape models have been used to determine features other than shape, such as volume, based on extrapolation from limited observations, Ruff C F, Bhalerao A, Hughes S W, D'Arcy T J, et. al., The estimation of fetal organ volume using statistical shape analysis. Computer Assisted Radiology. 1996, pp.280-5. Anatomical atlases, which provide physicians and surgeons with roadmaps to the body, are another incarnation of these models. In object segmentation, statistical models may serve as a prior in a probabilistic formulation of the task, Gonzalez B et. al., Combined statistical and geometrical 3D segmentation and measurement of brain structures. Workshop on Biomedical Image Analysis 1998, pp.14-23, or form the basis for constraining possible resultant deformations.
There has been a great deal of work in recent years on developing statistical shape models for morphological analysis, Bookstein F L. Shape and the information in medical images: a decade of the morphometric synthesis. [Journal Paper] CVIU, vol.66, no.2, May 1997, pp.97-118, Cootes T F, et. al., Flexible 3D models from uncalibrated cameras. Image & Vision Computing, vol.14, no.8, August 1996, pp.581-7.
Some work focuses on the problem of generating a dense set of landmarks semi-automatically. Fleute and Lavallee rigidly map several training examples of a femur to a densely sampled template instance, then use the inversions of these mappings to obtain corresponding points on each of the examples.
Lorenz and Krahnstover automatically locate candidates for landmarks via a metric for points of high curvature, Lorenz C, Krahnstover N. Generation of point-based 3D statistical shape models for anatomical objects. CVIU, vol.77, no.2, February 2000, pp.175-91. This method is not applicable to the heart because the LV of the hearts lacks such features.
Brett and Taylor merge decimated polygonal representations of objects in a tree-like fashion, Brett A D, Taylor C J. A method of automated landmark generation for automated 3D PDM construction. Image & Vision Computing, vol.18, no.9, June 2000, pp.739-48. Associations are made between objects via a symmetric Iterative Closest Point (ICP) algorithm. The ICP algorithm calls for re-mapping the polygonal structures.
Kelemen, Szekely and Gerig express their statistical shape models in terms of spherical harmonics and include the pose of the organ under study as part of the model, Kelemen A, et. al., Three-dimensional model-based segmentation of brain MRI. Workshop on Biomedical Image Analysis IEEE Comput. Soc. 1998, pp.4-13.
However, the prior art does not take into account the lack of identifiable landmarks and sparse inputs. Therefore, a need exists for a system and method for creating a 3-D statistical shape model of the left ventricle from sets of sparse 2-D contour inputs.